Person:

Rifas-Shiman, Sheryl

Introduction: Harboring sensitive strains may prevent acquisition of resistant pathogens by competing for colonization of ecological niches. Competition may be relevant to decolonization strategies that eliminate sensitive strains and may predispose to acquiring resistant strains in high-endemic settings. We evaluated the impact of colonization with methicillin-sensitive Staphylococcus aureus(MSSA) and vancomycin-sensitive enterococci (VSE) on acquisition of methicillin-resistantStaphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE), respectively, when controlling for other risk factors. Methods: We conducted a nested case-control study of patients admitted to eight ICUs performing admission and weekly bilateral nares and rectal screening for MRSA and VRE, respectively. Analyses were identical for both pathogens. For MRSA, patients were identified who had a negative nares screen and no prior history of MRSA. We evaluated predictors of MRSA acquisition, defined as a subsequent MRSA-positive clinical or screening culture, compared to those with a subsequent MRSA-negative nares screen within the same hospitalization. Medical records were reviewed for the presence of MSSA on the initial MRSA-negative nares screen, demographic and comorbidity information, medical devices, procedures, antibiotic utilization, and daily exposure to MRSA-positive patients in the same ward. Generalized linear mixed models were used to assess predictors of acquisition. Results: In multivariate models, MSSA carriage protected against subsequent MRSA acquisition (OR = 0.52, CI: 0.29, 0.95), even when controlling for other risk factors. MRSA predictors included intubation (OR = 4.65, CI: 1.77, 12.26), fluoroquinolone exposure (OR = 1.91, CI: 1.20, 3.04), and increased time from ICU admission to initial negative swab (OR = 15.59, CI: 8.40, 28.94). In contrast, VSE carriage did not protect against VRE acquisition (OR = 1.37, CI: 0.54, 3.48), whereas hemodialysis (OR = 2.60, CI: 1.19, 5.70), low albumin (OR = 2.07, CI: 1.12, 3.83), fluoroquinolones (OR = 1.90, CI: 1.14, 3.17), third-generation cephalosporins (OR = 1.89, CI: 1.15, 3.10), and increased time from ICU admission to initial negative swab (OR = 15.13, CI: 7.86, 29.14) were predictive. Conclusions: MSSA carriage reduced the odds of MRSA acquisition by 50% in ICUs. In contrast, VSE colonization was not protective against VRE acquisition. Studies are needed to evaluate whether decolonization of MSSA ICU carriers increases the risk of acquiring MRSA when discharging patients to high-endemic MRSA healthcare settings. This may be particularly important for populations in whom MRSA infection may be more frequent and severe than MSSA infections, such as ICU patients.

Objective: To investigate estimation of calorie (energy) content of meals from fast food restaurants in adults, adolescents, and school age children. Design: Cross sectional study of repeated visits to fast food restaurant chains. Setting: 89 fast food restaurants in four cities in New England, United States: McDonald’s, Burger King, Subway, Wendy’s, KFC, Dunkin’ Donuts. Participants: 1877 adults and 330 school age children visiting restaurants at dinnertime (evening meal) in 2010 and 2011; 1178 adolescents visiting restaurants after school or at lunchtime in 2010 and 2011. Main outcome measure Estimated calorie content of purchased meals. Results: Among adults, adolescents, and school age children, the mean actual calorie content of meals was 836 calories (SD 465), 756 calories (SD 455), and 733 calories (SD 359), respectively. A calorie is equivalent to 4.18 kJ. Compared with the actual figures, participants underestimated calorie content by means of 175 calories (95% confidence interval 145 to 205), 259 calories (227 to 291), and 175 calories (108 to 242), respectively. In multivariable linear regression models, underestimation of calorie content increased substantially as the actual meal calorie content increased. Adults and adolescents eating at Subway estimated 20% and 25% lower calorie content than McDonald’s diners (relative change 0.80, 95% confidence interval 0.66 to 0.96; 0.75, 0.57 to 0.99). Conclusions: People eating at fast food restaurants underestimate the calorie content of meals, especially large meals. Education of consumers through calorie menu labeling and other outreach efforts might reduce the large degree of underestimation.

Background: Obesity is associated with increased cardiovascular diseases and diabetes mellitus. Guidelines call for intensified glucose and lipid screening among overweight and obese patients. Data on compliance with these guidelines are scarce. The purpose of this study was to assess rates of diabetes and lipid screening in primary care according to demographic variables and weight status.Methods Over a 3-year follow-up period, we assessed screening rates for blood glucose, triglycerides, and HDL- and LDL-cholesterol among 5025 patients in primary care. From proportional hazards models we estimated screening rates among low, moderate, high, and very-high risk patients and compared them with recommendations of the American Diabetes Association (ADA), National Cholesterol Education Program (ATP III) and U.S. Preventive Services Task Force (USPSTF). Results: Mean (SD) age was 47.4 (15.6); 69% were female, 21% were non-white, and 30% of males and 25% of females were obese (BMI ≥ 30 kg/m2). For both diabetes and lipid screening, the adjusted hazard was 260–330% higher among ≥65 than <35 year-olds, 50–90% higher in persons with BMI ≥ 35 than <25 kg/m2, 10–30% lower for females than males, and not lower among racial/ethnic minorities. Screening rates were at least 80% among very-high risk persons, which we defined as 55–64 years old, BMI ≥ 35 kg/m2, non-white, with baseline hypertension. In contrast, high-risk persons who were younger (35–44 years old) and less obese (BMI 30–<35 kg/m2) were screened less often (43% for LDL-cholesterol among females to 83% for diabetes among males) even though ADA, ATP III and USPSTF recommend diabetes and lipid screening among them. Conclusion: Patients with higher BMI or age were more likely to be screened for cardiometabolic risk factors. Women were screened at lower rates than men. Even in a highly structured medical group practice, some obese patients were under-screened for diabetes and dyslipidemia.

Background: Excessive gestational weight gain promotes poor maternal and child health outcomes. Weight misperception is associated with weight gain in non-pregnant women, but no data exist during pregnancy. The purpose of this study was to examine the association of misperceived pre-pregnancy body weight status with excessive gestational weight gain. Methods: At study enrollment, participants in Project Viva reported weight, height, and perceived body weight status by questionnaire. Our study sample comprised 1537 women who had either normal or overweight/obese pre-pregnancy BMI. We created 2 categories of pre-pregnancy body weight status misperception: normal weight women who identified themselves as overweight ('overassessors') and overweight/obese women who identified themselves as average or underweight ('underassessors'). Women who correctly perceived their body weight status were classified as either normal weight or overweight/obese accurate assessors. We performed multivariable logistic regression to determine the odds of excessive gestational weight gain according to 1990 Institute of Medicine guidelines. Results: Of the 1029 women with normal pre-pregnancy BMI, 898 (87%) accurately perceived and 131 (13%) overassessed their weight status. 508 women were overweight/obese, of whom 438 (86%) accurately perceived and 70 (14%) underassessed their pre-pregnancy weight status. By the end of pregnancy, 823 women (54%) gained excessively. Compared with normal weight accurate assessors, the adjusted odds of excessive gestational weight gain was 2.0 (95% confidence interval [CI]: 1.3, 3.0) in normal weight overassessors, 2.9 (95% CI: 2.2, 3.9) in overweight/obese accurate assessors, and 7.6 (95% CI: 3.4, 17.0) in overweight/obese underassessors. Conclusion: Misperceived pre-pregnancy body weight status was associated with excessive gestational weight gain among both normal weight and overweight/obese women, with the greatest likelihood of excessive gain among overweight/obese underassessors. Future interventions should test the potential benefits of correcting misperception to reduce the likelihood of excessive gestational weight gain.

Background: Modeling childhood body mass index (BMI) trajectories, versus estimating change in BMI between specific ages, may improve prediction of later body-size-related outcomes. Prior studies of BMI trajectories are limited by restricted age periods and insufficient use of trajectory information. Methods: Among 3,289 children seen at 81,550 pediatric well-child visits from infancy to 18 years between 1980 and 2008, we fit individual BMI trajectories using mixed effect models with fractional polynomial functions. From each child's fitted trajectory, we estimated age and BMI at infancy peak and adiposity rebound, and velocity and area under curve between 1 week, infancy peak, adiposity rebound, and 18 years. Results: Among boys, mean (SD) ages at infancy BMI peak and adiposity rebound were 7.2 (0.9) and 49.2 (11.9) months, respectively. Among girls, mean (SD) ages at infancy BMI peak and adiposity rebound were 7.4 (1.1) and 46.8 (11.0) months, respectively. Ages at infancy peak and adiposity rebound were weakly inversely correlated (r = -0.09). BMI at infancy peak and adiposity rebound were positively correlated (r = 0.76). Blacks had earlier adiposity rebound and greater velocity from adiposity rebound to 18 years of age than whites. Higher birth weight z-score predicted earlier adiposity rebound and higher BMI at infancy peak and adiposity rebound. BMI trajectories did not differ by birth year or type of health insurance, after adjusting for other socio-demographics and birth weight z-score. Conclusions: Childhood BMI trajectory characteristics are informative in describing childhood body mass changes and can be estimated conveniently. Future research should evaluate associations of these novel BMI trajectory characteristics with adult outcomes.

Objective: Lactation has been associated with reduced maternal risk of type 2 diabetes, the metabolic syndrome, and cardiovascular disease. We examined the relationship between breastfeeding duration and maternal adipokines at 3 years postpartum. Research Design and Methods: We used linear regression to relate the duration of lactation to maternal leptin, adiponectin, ghrelin, and peptide YY (PYY) at 3 years postpartum among 570 participants with 3-year postpartum blood samples (178 fasting), prospectively collected lactation history, and no intervening pregnancy in Project Viva, a cohort study of mothers and children. Results: A total of 88% of mothers had initiated breastfeeding, 26% had breastfed (\geq 12) months, and 42% had exclusively breastfed for (\geq 3) months. In multivariate analyses, we found that duration of total breastfeeding was directly related to PYY and ghrelin, and exclusive breastfeeding duration was directly related to ghrelin (predicted mean for never exclusively breastfeeding: 790.6 pg/mL vs. (\geq 6) months of exclusive breastfeeding: 1,008.1 pg/mL; P < 0.01) at 3 years postpartum, adjusting for pregravid BMI, gestational weight gain, family history of diabetes, parity, smoking status, and age. We found a nonlinear pattern of association between exclusive breastfeeding duration and adiponectin in multivariate-adjusted models. Conclusions: In this prospective cohort study, we found a direct relationship between the duration of lactation and both ghrelin and PYY at 3 years postpartum.

OBJECTIVE To examine the associations of maternal gestational glucose tolerance with offspring body composition in late childhood. RESEARCH DESIGN AND METHODS Among 958 women in the prebirth cohort Project Viva, glucose tolerance was assessed in the second trimester by nonfasting 50-g 1-h glucose challenge test (GCT), followed if abnormal by fasting 100-g 3-h oral glucose tolerance test (OGTT). We categorized women as normoglycemic (83.3%) if GCT was ≤140 mg/dL, isolated hyperglycemia (9.1%) if GCT was abnormal but OGTT normal, intermediate glucose intolerance (IGI) (3.3%) if there was one abnormal value on OGTT, or gestational diabetes mellitus (GDM) (4.5%) if there were two or more abnormal OGTT values. Using multivariable linear regression, we examined adjusted associations of glucose tolerance with offspring overall (N = 958) and central (N = 760) adiposity and body composition using dual X-ray absorptiometry (DXA) measured at the school-age visit (95 ± 10 months). RESULTS Compared with that in the male offspring of normoglycemic mothers, DXA fat mass was higher in male offspring of GDM mothers (1.89 kg [95% CI 0.33–3.45]) but not in male offspring of mothers with IGI (0.06 kg [−1.45 to 1.57]). DXA trunk-to-peripheral fat mass, a measure of central adiposity, was also somewhat higher in male offspring of GDM mothers (0.04 [−0.01 to 0.09]). In girls, DXA fat mass was higher in offspring of mothers with IGI (2.23 kg [0.12–4.34]) but not GDM (−1.25 kg [−3.13 to 0.63]). We showed no association of gestational glucose tolerance with DXA lean mass. CONCLUSIONS In this study, only male offspring of GDM mothers manifested increased adiposity, whereas only female offspring of mothers with IGI did so. Sex differences in glycemic sensitivity may explain these findings.

Background: Childhood obesity remains a prominent public health problem. Walkable built environments may prevent excess weight gain. Objectives: We examined the association of walkable built environment characteristics with body mass index (BMI) z-score among a large sample of children and adolescents. Methods: We used geocoded residential address data from electronic health records of 49,770 children and adolescents 4 to < 19 years of age seen at the 14 pediatric practices of Harvard Vanguard Medical Associates from August 2011 through August 2012. We used eight geographic information system (GIS) variables to characterize walkable built environments. Outcomes were BMI z-score at the most recent visit and BMI z-score change from the earliest available (2008–2011) to the most recent (2011–2012) visit. Multivariable models were adjusted for child age, sex, race/ethnicity, and neighborhood median household income. Results: In multivariable cross-sectional models, living in closer proximity to recreational open space was associated with lower BMI z-score. For example, children who lived in closest proximity (quartile 1) to the nearest recreational open space had a lower BMI z-score (β = –0.06; 95% CI: –0.08, –0.03) compared with those living farthest away (quartile 4; reference). Living in neighborhoods with fewer recreational open spaces and less residential density, traffic density, sidewalk completeness, and intersection density were associated with higher cross-sectional BMI z-score and with an increase in BMI z-score over time. Conclusions: Overall, built environment characteristics that may increase walkability were associated with lower BMI z-scores in a large sample of children. Modifying existing built environments to make them more walkable may reduce childhood obesity. Citation: Duncan DT, Sharifi M, Melly SJ, Marshall R, Sequist TD, Rifas-Shiman SL, Taveras EM. 2014. Characteristics of walkable built environments and BMI z-scores in children: evidence from a large electronic health record database. Environ Health Perspect 122:1359–1365; http://dx.doi.org/10.1289/ehp.1307704

Background: The role of fathers in the development of obesity in their offspring remains poorly understood. We evaluated associations of missing paternal demographic information on birth certificates with perinatal risk factors for childhood obesity. Methods: Data were from the Linked CENTURY Study, a database linking birth certificate and well-child visit data for 200,258 Massachusetts children from 1980–2008. We categorized participants based on the availability of paternal age, education, or race/ethnicity and maternal marital status on the birth certificate: (1) pregnancies missing paternal data; (2) pregnancies involving unmarried women with paternal data; and (3) pregnancies involving married women with paternal data. Using linear and logistic regression, we compared differences in smoking during pregnancy, gestational diabetes, birthweight, breastfeeding initiation, and ever recording a weight for length (WFL) ≥ the 95th percentile or crossing upwards ≥2 WFL percentiles between 0–24 months among the study groups. Results: 11,989 (6.0 %) birth certificates were missing paternal data; 31,323 (15.6 %) mothers were unmarried. In adjusted analyses, missing paternal data was associated with lower birthweight (β -0.07 kg; 95 % CI: −0.08, −0.05), smoking during pregnancy (AOR 4.40; 95 % CI: 3.97, 4.87), non-initiation of breastfeeding (AOR 0.39; 95 % CI: 0.36, 0.42), and with ever having a WFL ≥ 95th percentile (AOR 1.10; 95 % CI: 1.01, 1.20). Similar associations were noted for pregnancies involving unmarried women with paternal data, but differences were less pronounced. Conclusions: Missing paternal data on the birth certificate is associated with perinatal risk factors for childhood obesity. Efforts to understand and reduce obesity risk factors in early life may need to consider paternal factors.

Objective: We examined associations of perinatal and 3-year leptin with weight gain and adiposity through 7 years. Design and Methods In Project Viva, we assessed plasma leptin from mothers at 26–28 weeks’ gestation (n=893), umbilical cord vein at delivery (n=540), and children at 3 years (n=510) in relation to body mass index (BMI) z-score, waist circumference, skinfold thicknesses, and dual X-ray absorptiometry body fat. Results: 50.1% of children were male and 29.5% non-white. Mean(SD) maternal, cord, and age 3 leptin concentrations were 22.9(14.2), 8.8(6.4), and 1.8(1.7) ng/mL, respectively, and 3- and 7-year BMI z-scores were 0.46(1.00) and 0.35(0.97), respectively. After adjusting for parental and child characteristics, higher maternal and cord leptin was associated with less 3- year adiposity. For example, mean 3-year BMI z-score was 0.5 lower (95%CI:−0.7,−0.2; p-trend=0.003) among children whose mothers’ leptin concentrations were in the top vs. bottom quintile. In contrast, higher age 3 leptin was associated with greater weight gain and adiposity through age 7 [e.g., change in BMI z-score from 3 to 7 years was 0.2 units (95%CI:−0.0,0.4; p-trend=0.05)]. Conclusions: Higher perinatal leptin was associated with lower 3-year adiposity, whereas higher age 3 leptin was associated with greater weight gain and adiposity by 7 years.